Embodiments of the present invention relate to intra-prediction for an image. Digital video requires a large amount of data to represent each and every frame of a digital video sequence (e.g., series of frames) in an uncompressed manner. It is not feasible for most applications to transmit uncompressed digital video across computer networks because of bandwidth limitations. In addition, uncompressed digital video requires a large amount of storage space. The digital video is normally encoded in some manner to reduce the storage requirements and reduce the bandwidth requirements.
One technique for encoding digital video is interframe encoding. Interframe encoding exploits the fact that different frames of video typically include regions of pixels, normally selected as x by x blocks, that remain substantially the same. During the encoding process a motion vector interrelates the movement of a block of pixels in one frame to a block of similar pixels in another frame. Accordingly, the system is not required to encode the block of pixels twice, but rather encodes the block of pixels once and provides a motion vector to predict the other block of pixels.
Another technique for encoding digital video is intraframe encoding. Intraframe encoding encodes a frame or a portion thereof without reference to pixels in other frames.
Typically intraframe encoding encodes the frame, or portions thereof, on a block by block basis. For example, m MEPG-2 the intraframe encoding makes use of discrete cosine transforms of a block of pixels and subsequent encoding of the transformed coefficients. Other intraframe encoding techniques exist, such as for example, wavelet encoding.
In general, these techniques employ relatively large data tables for referencing prediction modes. Memory for these data tables can be burdensomely expensive for many low cost machines. Moreover, it is also burdensomely expensive to provide sufficient memory within processing devices to store the data table. Also, the resulting system has increased complexity with the large data table.